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| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2505.22705 |
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| _version_ | 1866912400624582656 |
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| author | Cai, Qi Chen, Jingwen Chen, Yang Li, Yehao Long, Fuchen Pan, Yingwei Qiu, Zhaofan Zhang, Yiheng Gao, Fengbin Xu, Peihan Wang, Yimeng Yu, Kai Chen, Wenxuan Feng, Ziwei Gong, Zijian Pan, Jianzhuang Peng, Yi Tian, Rui Wang, Siyu Zhao, Bo Yao, Ting Mei, Tao |
| author_facet | Cai, Qi Chen, Jingwen Chen, Yang Li, Yehao Long, Fuchen Pan, Yingwei Qiu, Zhaofan Zhang, Yiheng Gao, Fengbin Xu, Peihan Wang, Yimeng Yu, Kai Chen, Wenxuan Feng, Ziwei Gong, Zijian Pan, Jianzhuang Peng, Yi Tian, Rui Wang, Siyu Zhao, Bo Yao, Ting Mei, Tao |
| contents | Recent advancements in image generative foundation models have prioritized quality improvements but often at the cost of increased computational complexity and inference latency. To address this critical trade-off, we introduce HiDream-I1, a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds. HiDream-I1 is constructed with a new sparse Diffusion Transformer (DiT) structure. Specifically, it starts with a dual-stream decoupled design of sparse DiT with dynamic Mixture-of-Experts (MoE) architecture, in which two separate encoders are first involved to independently process image and text tokens. Then, a single-stream sparse DiT structure with dynamic MoE architecture is adopted to trigger multi-model interaction for image generation in a cost-efficient manner. To support flexiable accessibility with varied model capabilities, we provide HiDream-I1 in three variants: HiDream-I1-Full, HiDream-I1-Dev, and HiDream-I1-Fast.
Furthermore, we go beyond the typical text-to-image generation and remould HiDream-I1 with additional image conditions to perform precise, instruction-based editing on given images, yielding a new instruction-based image editing model namely HiDream-E1. Ultimately, by integrating text-to-image generation and instruction-based image editing, HiDream-I1 evolves to form a comprehensive image agent (HiDream-A1) capable of fully interactive image creation and refinement. To accelerate multi-modal AIGC research, we have open-sourced all the codes and model weights of HiDream-I1-Full, HiDream-I1-Dev, HiDream-I1-Fast, HiDream-E1 through our project websites: https://github.com/HiDream-ai/HiDream-I1 and https://github.com/HiDream-ai/HiDream-E1. All features can be directly experienced via https://vivago.ai/studio. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22705 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer Cai, Qi Chen, Jingwen Chen, Yang Li, Yehao Long, Fuchen Pan, Yingwei Qiu, Zhaofan Zhang, Yiheng Gao, Fengbin Xu, Peihan Wang, Yimeng Yu, Kai Chen, Wenxuan Feng, Ziwei Gong, Zijian Pan, Jianzhuang Peng, Yi Tian, Rui Wang, Siyu Zhao, Bo Yao, Ting Mei, Tao Computer Vision and Pattern Recognition Multimedia Recent advancements in image generative foundation models have prioritized quality improvements but often at the cost of increased computational complexity and inference latency. To address this critical trade-off, we introduce HiDream-I1, a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds. HiDream-I1 is constructed with a new sparse Diffusion Transformer (DiT) structure. Specifically, it starts with a dual-stream decoupled design of sparse DiT with dynamic Mixture-of-Experts (MoE) architecture, in which two separate encoders are first involved to independently process image and text tokens. Then, a single-stream sparse DiT structure with dynamic MoE architecture is adopted to trigger multi-model interaction for image generation in a cost-efficient manner. To support flexiable accessibility with varied model capabilities, we provide HiDream-I1 in three variants: HiDream-I1-Full, HiDream-I1-Dev, and HiDream-I1-Fast. Furthermore, we go beyond the typical text-to-image generation and remould HiDream-I1 with additional image conditions to perform precise, instruction-based editing on given images, yielding a new instruction-based image editing model namely HiDream-E1. Ultimately, by integrating text-to-image generation and instruction-based image editing, HiDream-I1 evolves to form a comprehensive image agent (HiDream-A1) capable of fully interactive image creation and refinement. To accelerate multi-modal AIGC research, we have open-sourced all the codes and model weights of HiDream-I1-Full, HiDream-I1-Dev, HiDream-I1-Fast, HiDream-E1 through our project websites: https://github.com/HiDream-ai/HiDream-I1 and https://github.com/HiDream-ai/HiDream-E1. All features can be directly experienced via https://vivago.ai/studio. |
| title | HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2505.22705 |